{
 "cells": [
  {
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error,f1_score,accuracy_score\n",
    "# from scipy.interpolate import spline\n",
    ""
   ],
   "cell_type": "code",
   "outputs": [],
   "metadata": {},
   "execution_count": 1
  },
  {
   "source": [
    "data=pd.read_csv(\"log.csv\")"
   ],
   "cell_type": "code",
   "outputs": [],
   "metadata": {},
   "execution_count": 2
  },
  {
   "source": [
    "data.head(20)"
   ],
   "cell_type": "code",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    Temperature  Pressure  Humidity\n0         24.46    989.15        56\n1         24.46    989.16        56\n2         24.46    989.07        56\n3         24.48    989.10        56\n4         24.48    989.11        56\n5         24.49    989.18        56\n6         24.51    989.20        56\n7         24.49    989.11        56\n8         24.49    989.11        56\n9         24.49    989.15        56\n10        24.49    989.11        56\n11        24.49    989.11        56\n12        24.49    989.13        56\n13        24.49    989.17        56\n14        24.48    989.11        56\n15        24.48    989.12        56\n16        24.49    989.12        56\n17        24.49    989.15        56\n18        24.50    989.22        56\n19        24.49    989.09        56",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature</th>\n      <th>Pressure</th>\n      <th>Humidity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>24.46</td>\n      <td>989.15</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>24.46</td>\n      <td>989.16</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>24.46</td>\n      <td>989.07</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>24.48</td>\n      <td>989.10</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.48</td>\n      <td>989.11</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>24.49</td>\n      <td>989.18</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>24.51</td>\n      <td>989.20</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>24.49</td>\n      <td>989.15</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>24.49</td>\n      <td>989.13</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>24.49</td>\n      <td>989.17</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>24.48</td>\n      <td>989.11</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>24.48</td>\n      <td>989.12</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>24.49</td>\n      <td>989.12</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>24.49</td>\n      <td>989.15</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>24.50</td>\n      <td>989.22</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>24.49</td>\n      <td>989.09</td>\n      <td>56</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {},
   "execution_count": 3
  },
  {
   "source": [
    "data.describe()"
   ],
   "cell_type": "code",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       Temperature    Pressure    Humidity\ncount   103.000000  103.000000  103.000000\nmean     24.462816  989.169903   55.970874\nstd       0.086766    0.044048    0.168983\nmin      24.190000  989.060000   55.000000\n25%      24.480000  989.140000   56.000000\n50%      24.500000  989.170000   56.000000\n75%      24.510000  989.200000   56.000000\nmax      24.540000  989.280000   56.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature</th>\n      <th>Pressure</th>\n      <th>Humidity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>103.000000</td>\n      <td>103.000000</td>\n      <td>103.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>24.462816</td>\n      <td>989.169903</td>\n      <td>55.970874</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.086766</td>\n      <td>0.044048</td>\n      <td>0.168983</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>24.190000</td>\n      <td>989.060000</td>\n      <td>55.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>24.480000</td>\n      <td>989.140000</td>\n      <td>56.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>24.500000</td>\n      <td>989.170000</td>\n      <td>56.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>24.510000</td>\n      <td>989.200000</td>\n      <td>56.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>24.540000</td>\n      <td>989.280000</td>\n      <td>56.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {},
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=data.iloc[:,2]\n",
    "df1=data.iloc[:,1]\n",
    "df0=data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<function matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Temperature'],data['Humidity'])\n",
    "# plt.scatter(df2,df1)\n",
    "plt.xlabel('Temperature')\n",
    "plt.ylabel('Humidity')\n",
    "plt.plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<function matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Temperature'],data['Pressure'])\n",
    "# plt.scatter(df2,df1)\n",
    "plt.xlabel('Temperature')\n",
    "plt.ylabel('Pressure')\n",
    "plt.plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kernels.py:128: RuntimeWarning: divide by zero encountered in true_divide\n  return (1. / np.sqrt(2 * np.pi)) * np.exp(-(Xi - x)**2 / (h**2 * 2.))\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kernels.py:128: RuntimeWarning: invalid value encountered in true_divide\n  return (1. / np.sqrt(2 * np.pi)) * np.exp(-(Xi - x)**2 / (h**2 * 2.))\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\_kernel_base.py:513: RuntimeWarning: invalid value encountered in true_divide\n  dens = Kval.prod(axis=1) / np.prod(bw[iscontinuous])\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n  self.zmax = float(z.max())\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n  self.zmin = float(z.min())\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:1203: RuntimeWarning: invalid value encountered in less\n  under = np.nonzero(lev < self.zmin)[0]\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py:1205: RuntimeWarning: invalid value encountered in greater\n  over = np.nonzero(lev > self.zmax)[0]\n"
    },
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.JointGrid at 0x1f3128d9940>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": "<Figure size 432x432 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=df0, y=df2,kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.JointGrid at 0x1f31297e8d0>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x432 with 3 Axes>"
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   ],
   "source": [
    "sns.jointplot(x=df0, y=df1,kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1f312d67630>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=df2, y=df1, data=data, jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1f312dc9198>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=df0, y=df1, data=data, jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x1f312db40f0>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 540x540 with 12 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gauss_distri=[]\n",
    "\n",
    "for val in range(0,len(data)):    \n",
    "    value=random.uniform(-1,1)\n",
    "    gauss_distri.append(value)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gauss']=gauss_distri\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Wind_Load']=0.0001 * 2 * 0.00256 * (7.5314+data['gauss'])**2 \n",
    "data['Wind_Pressure']=data['Wind_Load'].values / 0.0002\n",
    "data['Wind_Speed']= ( data['Wind_Pressure'].values / 0.00256 )**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature</th>\n      <th>Pressure</th>\n      <th>Humidity</th>\n      <th>gauss</th>\n      <th>Wind_Load</th>\n      <th>Wind_Pressure</th>\n      <th>Wind_Speed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>24.46</td>\n      <td>989.15</td>\n      <td>56</td>\n      <td>0.070780</td>\n      <td>0.000030</td>\n      <td>0.147950</td>\n      <td>7.602180</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>24.46</td>\n      <td>989.16</td>\n      <td>56</td>\n      <td>0.256940</td>\n      <td>0.000031</td>\n      <td>0.155285</td>\n      <td>7.788340</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>24.46</td>\n      <td>989.07</td>\n      <td>56</td>\n      <td>-0.636426</td>\n      <td>0.000024</td>\n      <td>0.121704</td>\n      <td>6.894974</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>24.48</td>\n      <td>989.10</td>\n      <td>56</td>\n      <td>0.446701</td>\n      <td>0.000033</td>\n      <td>0.162944</td>\n      <td>7.978101</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.48</td>\n      <td>989.11</td>\n      <td>56</td>\n      <td>-0.862480</td>\n      <td>0.000023</td>\n      <td>0.113855</td>\n      <td>6.668920</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>24.49</td>\n      <td>989.18</td>\n      <td>56</td>\n      <td>0.753128</td>\n      <td>0.000035</td>\n      <td>0.175702</td>\n      <td>8.284528</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>24.51</td>\n      <td>989.20</td>\n      <td>56</td>\n      <td>-0.811713</td>\n      <td>0.000023</td>\n      <td>0.115595</td>\n      <td>6.719687</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n      <td>0.530874</td>\n      <td>0.000033</td>\n      <td>0.166401</td>\n      <td>8.062274</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n      <td>-0.590909</td>\n      <td>0.000025</td>\n      <td>0.123316</td>\n      <td>6.940491</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>24.49</td>\n      <td>989.15</td>\n      <td>56</td>\n      <td>-0.440816</td>\n      <td>0.000026</td>\n      <td>0.128708</td>\n      <td>7.090584</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   Temperature  Pressure  Humidity     gauss  Wind_Load  Wind_Pressure  \\\n0        24.46    989.15        56  0.070780   0.000030       0.147950   \n1        24.46    989.16        56  0.256940   0.000031       0.155285   \n2        24.46    989.07        56 -0.636426   0.000024       0.121704   \n3        24.48    989.10        56  0.446701   0.000033       0.162944   \n4        24.48    989.11        56 -0.862480   0.000023       0.113855   \n5        24.49    989.18        56  0.753128   0.000035       0.175702   \n6        24.51    989.20        56 -0.811713   0.000023       0.115595   \n7        24.49    989.11        56  0.530874   0.000033       0.166401   \n8        24.49    989.11        56 -0.590909   0.000025       0.123316   \n9        24.49    989.15        56 -0.440816   0.000026       0.128708   \n\n   Wind_Speed  \n0    7.602180  \n1    7.788340  \n2    6.894974  \n3    7.978101  \n4    6.668920  \n5    8.284528  \n6    6.719687  \n7    8.062274  \n8    6.940491  \n9    7.090584  "
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature</th>\n      <th>Pressure</th>\n      <th>Humidity</th>\n      <th>Wind_Load</th>\n      <th>Wind_Pressure</th>\n      <th>Wind_Speed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>24.46</td>\n      <td>989.15</td>\n      <td>56</td>\n      <td>0.000030</td>\n      <td>0.147950</td>\n      <td>7.602180</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>24.46</td>\n      <td>989.16</td>\n      <td>56</td>\n      <td>0.000031</td>\n      <td>0.155285</td>\n      <td>7.788340</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>24.46</td>\n      <td>989.07</td>\n      <td>56</td>\n      <td>0.000024</td>\n      <td>0.121704</td>\n      <td>6.894974</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>24.48</td>\n      <td>989.10</td>\n      <td>56</td>\n      <td>0.000033</td>\n      <td>0.162944</td>\n      <td>7.978101</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.48</td>\n      <td>989.11</td>\n      <td>56</td>\n      <td>0.000023</td>\n      <td>0.113855</td>\n      <td>6.668920</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>24.49</td>\n      <td>989.18</td>\n      <td>56</td>\n      <td>0.000035</td>\n      <td>0.175702</td>\n      <td>8.284528</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>24.51</td>\n      <td>989.20</td>\n      <td>56</td>\n      <td>0.000023</td>\n      <td>0.115595</td>\n      <td>6.719687</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n      <td>0.000033</td>\n      <td>0.166401</td>\n      <td>8.062274</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>24.49</td>\n      <td>989.11</td>\n      <td>56</td>\n      <td>0.000025</td>\n      <td>0.123316</td>\n      <td>6.940491</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>24.49</td>\n      <td>989.15</td>\n      <td>56</td>\n      <td>0.000026</td>\n      <td>0.128708</td>\n      <td>7.090584</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   Temperature  Pressure  Humidity  Wind_Load  Wind_Pressure  Wind_Speed\n0        24.46    989.15        56   0.000030       0.147950    7.602180\n1        24.46    989.16        56   0.000031       0.155285    7.788340\n2        24.46    989.07        56   0.000024       0.121704    6.894974\n3        24.48    989.10        56   0.000033       0.162944    7.978101\n4        24.48    989.11        56   0.000023       0.113855    6.668920\n5        24.49    989.18        56   0.000035       0.175702    8.284528\n6        24.51    989.20        56   0.000023       0.115595    6.719687\n7        24.49    989.11        56   0.000033       0.166401    8.062274\n8        24.49    989.11        56   0.000025       0.123316    6.940491\n9        24.49    989.15        56   0.000026       0.128708    7.090584"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del data['gauss']\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       color:  #f1f1f1;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col5 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col0 {\n            background-color:  #bed2f6;\n            color:  #000000;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col1 {\n            background-color:  #9abbff;\n            color:  #000000;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col2 {\n            background-color:  #92b4fe;\n            color:  #000000;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col3 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col4 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col5 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }</style><table id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Temperature</th>        <th class=\"col_heading level0 col1\" >Pressure</th>        <th class=\"col_heading level0 col2\" >Humidity</th>        <th class=\"col_heading level0 col3\" >Wind_Load</th>        <th class=\"col_heading level0 col4\" >Wind_Pressure</th>        <th class=\"col_heading level0 col5\" >Wind_Speed</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982level0_row0\" class=\"row_heading level0 row0\" >Temperature</th>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row0_col0\" class=\"data row0 col0\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row0_col1\" class=\"data row0 col1\" >-0.476296</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row0_col2\" class=\"data row0 col2\" >0.513835</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row0_col3\" class=\"data row0 col3\" >0.104413</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row0_col4\" class=\"data row0 col4\" >0.104413</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row0_col5\" class=\"data row0 col5\" >0.102244</td>\n            </tr>\n            <tr>\n                        <th id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982level0_row1\" class=\"row_heading level0 row1\" >Pressure</th>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row1_col0\" class=\"data row1 col0\" >-0.476296</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row1_col1\" class=\"data row1 col1\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row1_col2\" class=\"data row1 col2\" >-0.171613</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row1_col3\" class=\"data row1 col3\" >-0.0463983</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row1_col4\" class=\"data row1 col4\" >-0.0463983</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row1_col5\" class=\"data row1 col5\" >-0.0512583</td>\n            </tr>\n            <tr>\n                        <th id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982level0_row2\" class=\"row_heading level0 row2\" >Humidity</th>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row2_col0\" class=\"data row2 col0\" >0.513835</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row2_col1\" class=\"data row2 col1\" >-0.171613</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row2_col2\" class=\"data row2 col2\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row2_col3\" class=\"data row2 col3\" >0.13944</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row2_col4\" class=\"data row2 col4\" >0.13944</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row2_col5\" class=\"data row2 col5\" >0.137763</td>\n            </tr>\n            <tr>\n                        <th id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982level0_row3\" class=\"row_heading level0 row3\" >Wind_Load</th>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row3_col0\" class=\"data row3 col0\" >0.104413</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row3_col1\" class=\"data row3 col1\" >-0.0463983</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row3_col2\" class=\"data row3 col2\" >0.13944</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row3_col3\" class=\"data row3 col3\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row3_col4\" class=\"data row3 col4\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row3_col5\" class=\"data row3 col5\" >0.999456</td>\n            </tr>\n            <tr>\n                        <th id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982level0_row4\" class=\"row_heading level0 row4\" >Wind_Pressure</th>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col0\" class=\"data row4 col0\" >0.104413</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col1\" class=\"data row4 col1\" >-0.0463983</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col2\" class=\"data row4 col2\" >0.13944</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col3\" class=\"data row4 col3\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col4\" class=\"data row4 col4\" >1</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row4_col5\" class=\"data row4 col5\" >0.999456</td>\n            </tr>\n            <tr>\n                        <th id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982level0_row5\" class=\"row_heading level0 row5\" >Wind_Speed</th>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col0\" class=\"data row5 col0\" >0.102244</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col1\" class=\"data row5 col1\" >-0.0512583</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col2\" class=\"data row5 col2\" >0.137763</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col3\" class=\"data row5 col3\" >0.999456</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col4\" class=\"data row5 col4\" >0.999456</td>\n                        <td id=\"T_eb0bf6f8_1a4c_11ea_b305_8aabcad71982row5_col5\" class=\"data row5 col5\" >1</td>\n            </tr>\n    </tbody></table>",
      "text/plain": "<pandas.io.formats.style.Styler at 0x1f31335a6d8>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Temperature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Pressure</th>\n      <td>-0.476296</td>\n    </tr>\n    <tr>\n      <th>Wind_Speed</th>\n      <td>0.102244</td>\n    </tr>\n    <tr>\n      <th>Wind_Pressure</th>\n      <td>0.104413</td>\n    </tr>\n    <tr>\n      <th>Wind_Load</th>\n      <td>0.104413</td>\n    </tr>\n    <tr>\n      <th>Humidity</th>\n      <td>0.513835</td>\n    </tr>\n    <tr>\n      <th>Temperature</th>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "               Temperature\nPressure         -0.476296\nWind_Speed        0.102244\nWind_Pressure     0.104413\nWind_Load         0.104413\nHumidity          0.513835\nTemperature       1.000000"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()[['Temperature']].sort_values('Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:,1:]\n",
    "y=data.iloc[:,0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Pressure  Humidity  Wind_Load  Wind_Pressure  Wind_Speed\n0      989.15        56   0.000030       0.147950    7.602180\n1      989.16        56   0.000031       0.155285    7.788340\n2      989.07        56   0.000024       0.121704    6.894974\n3      989.10        56   0.000033       0.162944    7.978101\n4      989.11        56   0.000023       0.113855    6.668920\n5      989.18        56   0.000035       0.175702    8.284528\n6      989.20        56   0.000023       0.115595    6.719687\n7      989.11        56   0.000033       0.166401    8.062274\n8      989.11        56   0.000025       0.123316    6.940491\n9      989.15        56   0.000026       0.128708    7.090584\n10     989.11        56   0.000025       0.124430    6.971771\n11     989.11        56   0.000026       0.131645    7.171044\n12     989.13        56   0.000027       0.135544    7.276449\n13     989.17        56   0.000031       0.155233    7.787040\n14     989.11        56   0.000027       0.132771    7.201656\n15     989.12        56   0.000028       0.139776    7.389172\n16     989.12        56   0.000027       0.136363    7.298409\n17     989.15        56   0.000030       0.148104    7.606133\n18     989.22        56   0.000022       0.111681    6.604955\n19     989.09        56   0.000032       0.159040    7.881937\n20     989.15        56   0.000032       0.162193    7.959682\n21     989.20        56   0.000022       0.110714    6.576292\n22     989.11        56   0.000025       0.125743    7.008461\n23     989.13        56   0.000030       0.150343    7.663404\n24     989.15        56   0.000030       0.150621    7.670483\n25     989.16        56   0.000032       0.158704    7.873620\n26     989.11        56   0.000023       0.113356    6.654308\n27     989.15        56   0.000026       0.128737    7.091391\n28     989.06        56   0.000037       0.185173    8.504900\n29     989.17        56   0.000032       0.159637    7.896711\n..        ...       ...        ...            ...         ...\n73     989.13        56   0.000025       0.124634    6.977470\n74     989.20        56   0.000030       0.152113    7.708385\n75     989.14        56   0.000032       0.161606    7.945272\n76     989.13        56   0.000035       0.172669    8.212724\n77     989.19        56   0.000022       0.111462    6.598484\n78     989.14        56   0.000030       0.150736    7.673421\n79     989.15        56   0.000026       0.131548    7.168386\n80     989.16        56   0.000030       0.149023    7.629683\n81     989.21        56   0.000031       0.157077    7.833157\n82     989.21        56   0.000022       0.110995    6.584635\n83     989.22        56   0.000025       0.123697    6.951195\n84     989.21        56   0.000030       0.150727    7.673193\n85     989.25        56   0.000037       0.182822    8.450736\n86     989.28        56   0.000024       0.119478    6.831611\n87     989.26        56   0.000034       0.170175    8.153192\n88     989.17        56   0.000023       0.114704    6.693750\n89     989.18        56   0.000027       0.136293    7.296533\n90     989.24        56   0.000025       0.125330    6.996920\n91     989.21        56   0.000031       0.156169    7.810477\n92     989.22        56   0.000026       0.132182    7.185652\n93     989.22        56   0.000034       0.169142    8.128414\n94     989.19        56   0.000028       0.142141    7.451439\n95     989.21        56   0.000034       0.169136    8.128262\n96     989.21        56   0.000033       0.163196    7.984272\n97     989.26        56   0.000033       0.166614    8.067449\n98     989.23        56   0.000022       0.111798    6.608412\n99     989.25        56   0.000024       0.121252    6.882169\n100    989.27        55   0.000024       0.120099    6.849345\n101    989.21        55   0.000024       0.121719    6.895397\n102    989.16        55   0.000028       0.137880    7.338889\n\n[103 rows x 5 columns]\n"
    }
   ],
   "source": [
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree=4\n",
    "\n",
    "model=make_pipeline(PolynomialFeatures(degree),LinearRegression())\n",
    "model.fit(x_train,y_train)\n",
    "    \n",
    "y_pred=model.predict(x_test)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.05185667741866392"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.007323584177887275"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "1.0"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "24.452537860189164"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1f3146b6978>]"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test['Pressure'],y_test,color='blue')\n",
    "plt.plot(x_test['Pressure'],y_pred)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "0.007323584177887275\n-1.3029810485227347\n0.05185667741866392\n"
    }
   ],
   "source": [
    "print(mean_squared_error(y_test,y_pred) )\n",
    "print( r2_score(y_test,y_pred) )\n",
    "print( mean_absolute_error(y_test,y_pred) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('temp.csv')\n",
    "df['Humidity'] = df['Humidity'].fillna((df['Humidity'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pressure</th>\n      <th>Humidity</th>\n      <th>Wind_Load</th>\n      <th>Wind_Pressure</th>\n      <th>Wind_Speed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>989.20</td>\n      <td>56.0</td>\n      <td>0.000024</td>\n      <td>0.119739</td>\n      <td>6.839070</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>989.20</td>\n      <td>56.0</td>\n      <td>0.000024</td>\n      <td>0.119414</td>\n      <td>6.829800</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>989.24</td>\n      <td>56.0</td>\n      <td>0.000034</td>\n      <td>0.169940</td>\n      <td>8.147572</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>989.21</td>\n      <td>56.0</td>\n      <td>0.000035</td>\n      <td>0.173185</td>\n      <td>8.224991</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>989.17</td>\n      <td>56.0</td>\n      <td>0.000027</td>\n      <td>0.135762</td>\n      <td>7.282312</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   Pressure  Humidity  Wind_Load  Wind_Pressure  Wind_Speed\n0    989.20      56.0   0.000024       0.119739    6.839070\n1    989.20      56.0   0.000024       0.119414    6.829800\n2    989.24      56.0   0.000034       0.169940    8.147572\n3    989.21      56.0   0.000035       0.173185    8.224991\n4    989.17      56.0   0.000027       0.135762    7.282312"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gauss_distri=[]\n",
    "\n",
    "for val in range(0,len(df)):    \n",
    "    value=random.uniform(-1,1)\n",
    "    gauss_distri.append(value)  \n",
    "\n",
    "df['gauss']=gauss_distri\n",
    "df['Wind_Load']=0.0001 * 2 * 0.00256 * (7.5314+df['gauss'])**2 \n",
    "df['Wind_Pressure']=df['Wind_Load'].values / 0.0002\n",
    "df['Wind_Speed']= ( df['Wind_Pressure'].values / 0.00256 )**0.5\n",
    "del df['gauss']\n",
    "del df['Temperature']\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "24.451619458198547\n"
    }
   ],
   "source": [
    "print(model.predict(df).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_of_sample=len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "at=data.iloc[:,0].sum()/no_of_sample\n",
    "ah=data.iloc[:,2].sum()/no_of_sample\n",
    "ap=data.iloc[:,1].sum()/no_of_sample\n",
    "aws=data['Wind_Speed'].sum()/no_of_sample\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make 2 3 ifs for temp and then nested 3 ifs for humidity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Average Temperature = 24.462815533980574 C\nAverage Humidity = 55.970873786407765 %\nAverage Air Pressure = 989.1699029126215 mm of Hg\nAverage Wind Speed = 7.497075870740381 Km/hr\n---------------------------------------------------------------------------------------------------------------------\n                                           Weather Forecast : \nThe weather will be pleasantly normal and humid \nwith Extremly visible sky\n"
    }
   ],
   "source": [
    "print('Average Temperature =',at,'C')\n",
    "print('Average Humidity =',ah,'%')\n",
    "print('Average Air Pressure =',ap,'mm of Hg')\n",
    "print('Average Wind Speed =',aws,'Km/hr')\n",
    "\n",
    "\n",
    "print('---------------------------------------------------------------------------------------------------------------------')\n",
    "\n",
    "print('                                           Weather Forecast : ')\n",
    "\n",
    "\n",
    "if(at>35):\n",
    "    if(ah>80):\n",
    "        print('The weather will be hot and very humid')     \n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be hot and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be hot and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be hot and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')\n",
    "        \n",
    "elif(at>30 and at<35):\n",
    "    if(ah>80):\n",
    "        print('The weather will be warm and very humid')\n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be warm and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be warm and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be warm and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')\n",
    "elif(at>20 and at<30):\n",
    "    if(ah>80):\n",
    "        print('The weather will be pleasantly normal and very humid')\n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be pleasantly normal and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be pleasantly normal and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be pleasantly normal and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')\n",
    "elif(at>15 and at<20):\n",
    "    if(ah>80):\n",
    "        print('The weather will be cool and very humid')\n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be cool and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be cool and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be cool and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')\n",
    "elif(at>10 and at<15):\n",
    "    if(ah>80):\n",
    "        print('The weather will be cold and very humid')\n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be cold and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be cold and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be cold and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')\n",
    "elif(at>0 and at<10):\n",
    "    if(ah>80):\n",
    "        print('The weather will be freezing and very humid')\n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be freezing and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be freezing and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be freezing and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')\n",
    "else:\n",
    "    if(ah>80):\n",
    "        print('The weather will be EXTREMELY FREEZING COLD and very humid')\n",
    "    elif(ah>50 and ah<80):\n",
    "        print('The weather will be EXTREMELY FREEZING COLD and humid ')\n",
    "    elif(ah>20 and ah<50):\n",
    "        print('The weather will be EXTREMELY FREEZING COLD and somewhat humid')\n",
    "    elif(ah>0 and ah<20):\n",
    "        print('The weather will be EXTREMELY FREEZING COLD and dry')\n",
    "    else:\n",
    "        print('ARID DROUGHT CONDITION')          \n",
    "        \n",
    "if(ap>700 and ap< 800):\n",
    "    print(' and the sky will be clear as usual')\n",
    "elif(ap>800):\n",
    "    print('with Extremly visible sky')\n",
    "elif(ap>0 and ap<300):\n",
    "    print('with Stormy skies with possible hurricane')\n",
    "elif(ap>300 and ap<700):\n",
    "    print('with Turbid skies with mild disturbances')\n",
    "\n",
    "        "
   ]
  }
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